Statutory Entitlements as Property: Implications of Property Analysis Methods For Emissions Trading
Bibliographic record
Abstract
Legislatures are increasingly developing novel, tradeable statutory entitlements, such as transferable licences or allowances, to respond to a range of social and environmental issues. However, the statutes that establish such entitlements commonly overlook the nature and scope of the legal interests, personal or proprietary, which may exist in relation to an entitlement. As a result, courts are increasingly dealing with issues that stem from the uncertain legal nature of statutory entitlements. Issues that have arisen include whether a statute dealing with property transfers is applicable to a particular statutory entitlement, whether a regulator must pay compensation for withdrawing an entitlement or whether a statutory entitlement is capable of supporting rights that are enforceable against third parties. To determine the legal nature of statutory entitlements, courts undertake a property analysis that involves considering the attributes of a statutory entitlement against particular indicia of property. In this article, we focus on the diff erent conceptions of property and its indicia in the United States, Australia, the United Kingdom and Canada. This comparative analysis illustrates the distinct approaches being adopted to resolve the uncertain legal nature of statutory entitlements. Using emissions trading schemes as a case study, we explore how the diff erent property analyses adopted impact the rights and liabilities of parties as well as the functioning of statutory entitlement schemes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".